Spectral Efficiency of Wireless Network

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Techniques to Enhance SpectralEfficiency of OFDM Wireless

Systems

by

Suvra Sekhar Das, B.Eng.

Dissertation

Presented to the International Doctoral School of Technology and Science

in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

Aalborg University

7th. September 2007


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Supervisors:Professor Ramjee PrasadAssociate Professor Elisabeth de Carvalho

The Assessment Committee:Professor Preben Mogensen, Aalborg University, DenmarkProfessor Lajos Hanzo, University of Southampton, UK

Professor Shinsuke Hara, Osaka City University Japan

Moderator:Associate Professor Fleming B. Frederiksen, Aalborg University, Denmark

ISBN: 87-92078-07-9ISSN: 0908-1224

Copyright c September, 2007 bySuvra Sekhar DasCenter for TeleInFrastruktur (CTiF)Aalborg UniversityNiels Jernes Vej 129220 Aalborg OstDenmarke-mail: [email protected]

All rights reserved by the author. No part of the material protected by thiscopyright notice may be reproduced or utilized in any form or by any means,electronics or mechanical, including photocopying, recording, or by any informationstorage and retrieval system, without written permission from the author.


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Dedicated to my Parents, my Sister and Madhulipa.


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Center for TeleInFrastruktur (CTIF), Aalborg University


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Abstract

In recent years Orthogonal Frequency Division Multiplexing (OFDM) based tech-

nologies are in wide use for wireless communication systems. This is because OFDM

elegantly overcomes the adverse effects of frequency selective fading channels and of-

fers high spectral efficiency. Investigation of techniques to further enhance the spectral

efficiency of OFDM based wireless systems is the prime objective of this thesis.

As a first step, a comparison is made between OFDM and Multi Carrier Spread

Spectrum (MC-SS) scheme. OFDM is found to offer relatively better performance

than MC-SS under channel estimation and synchronization errors. This is the mo-

tivation to select OFDM for further investigation and performance enhancement in

this thesis.

Though OFDM has many advantages, yet it is severely affected by Inter Carrier

Interference (ICI), which is caused by residual phase error, carrier frequency offset

and Doppler frequency spread. To track the residual phase error, pilot sub carriers

are embedded between the data sub carriers. To reduce the pilot overhead, it is

proposed in this thesis to load data bits on pilot sub carriers without degrading

system performance. It is found that up to 15% increase in spectral efficiency can be

obtained by using this technique.

To mitigate the impact of ICI, due to Doppler frequency spread, a novel tech-

nique of using adaptive sub carrier bandwidth is proposed in this work. This technique

enhances the spectral efficiency in the range of 10% to 30% over OFDM systems which

use fixed sub carrier bandwidth.

OFDM systems need a large Guard Interval (GI) to overcome the effect of Inter

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Symbol Interference (ISI). In order to decrease the overhead due to GI, an algorithm

to dynamically select the GI duration is derived in this thesis. By adaptively selecting

the GI duration, it is found that the spectral efficiency can be increased up to 20%.

The performance of OFDM based wireless systems is also limited by the time

variations of the propagation channel. In such situations Link Adaptation (LA) tech-

niques using adaptive bit rate transmission achieves very high spectral efficiency by

exploiting the channel variations. OFDM provides a suitable framework for LA.

However, the combination of LA and OFDM results in increased implementation

complexity. Another aim of this thesis is to provide low complexity techniques to in-

crease spectral efficiency. Hence, low complexity, low overhead LA - OFDM schemes

which have near optimal spectral efficiency are proposed in this work. The proposed

schemes reduce overhead by 50% as well as significantly bring down the implementa-

tion complexity.

The impact of non linear signal distortion caused by the high power amplifier

and frequency synchronization errors on the performance of LA-OFDM systems are

also analyzed in this work. Methods to overcome the effects of these impairments by

suitable adjustments to the LA algorithms are presented in this thesis.

As a result of this work it can be concluded that significant cumulative gain in

spectral efficiency can be obtained by using the proposed transmission schemes. The

techniques and guidelines for spectral efficiency improvement presented in this thesis

are promising enough for future OFDM based wireless systems.



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Dansk Resume

I de seneste ar er Orthogonal Frequency Division Multiplexing (OFDM) baserede

systemer taget mere og mere i brug i forbindelse med tradlst kommunikationsudstyr.

Det skyldes, at OFDM pa en effektiv made kan hindre den delggende virkning af

frequency-selective-fading, og at OFDM samtidig kan tilbyde hj spektral effektivitet.

Det primre formal med denne afhandling er undersgelse af teknikker, som

yderligere kan forbedre den spektrale effektivitet af OFDM baserede tradlse sys-

temer. Som det frste trin gennemfres en sammenligning mellem OFDM og dets

udvidelsessystem MC-SS. OFDM ses her at tilbyde forholdsvis bedre ydeevne end

MC-SS m.h.t. modtagereffektivitet og -fejl. Derfor foretrkkes OFDM i denne afhan-

dling og undersges njere med henblik pa yderligere forbedring af ydeevnen.

OFDM har mange fordele men er umiddelbart strkt pavirket af Inter Carrier

Interference (ICI). ICI skyldes residuale fasefejl, carrier-frequency-offset og Doppler-

frequency-spread. For at spore de risiduale fasefejl er der indfrt pilot-sub-carriers

mellem de enkelte data-sub-carriers. Med sigte pa at mindske overheadet i forbindelse

med indfrelsen af pilot-sub-carriers er det i afhandlingen foreslaet at tilfre data bits

til de enkelte pilot-sub-carriers for dermed at undga reduktion af systemydeevnen.

Ved undersgelse findes det, at der med denne teknik kan opnas op til 15% forbedring

i spektral effektivitet.

For at mindske den betydelige virkning, som ICI ved Doppler-frequency-spread

har pa OFDM systemer, foreslas der i nrvrende arbejde en ny teknik baseret pa

adaptiv sub-carrier bandbredde. Der opnas med denne teknik en forbedring pa 10%

- 30% af den spektrale effektivitet i forhold til OFDM systemer med fast sub-carrier

bandbredde.

Der behves i OFDM systemer et stort Guard Interval (GI) for at overvinde

virkningen af Inter Symbol Interference (ISI). Med sigte pa at fa mindsket overheadet

i forbindelse med GI er der i afhandlingen udviklet en algoritme, som dynamisk

fastlgger GI-varigheden. Det vises, at hvis GI-varigheden vlges adaptivt v.h.a.

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algoritmen, kan den spektrale effektivitet ges op til 15%.

OFDM baserede tradlse systemer er ogsa begrnsede af den tidsvarierende

kanal. I sadanne situationer kan man ved hjlp af Link Adaptation (LA) teknikmed adaptiv bit-rate-transmission opna meget hj spektraleffektivitet ved at udnytte

variationer i kanalen. OFDM tilvejebringer en passende ramme for LA. Kombina-

tionen af LA og OFDM resulterer dog i forget implementationskompleksitet. Et

andet sigte med denne afhandling er derfor at tilvejebringe en lav-kompleksitets LA-

OFDM teknik. I nrvrende arbejde foreslas der af den grund lav-kompleksitets,

lille-overheads LA-OFDM systemer med nsten optimale spectrale effektiviteter. Det

beregnes, at de foreslaede systemer kan reducere overheadet med 50% samtidig med,

at kompleksiteten reduceres significant.

Virkningen af en praktisk funktionsbegrnsning - f.eks. i forbindelse med

ikke-liner signalforvrngning forarsaget af effektforstrkere og givet ved resultatet

af frekvens-synkroniseringsfejl i forbindelse med driften af LA-OFDM systemer - er

ogsa analyseret, og metoder til at fa bugt med disse forringelser og fejl ved passende

justering af LA algoritmerne er prsenteret i afhandlingen.

Som et resultat af arbejdet kan det konkluderes, at significant kumulativ

forbedring af den spektrale effektivitet kan opnas ved brug af de foreslaede adap-

tive transmissionsteknikker. De adaptive teknikker og retningslinier for forbedring

af den spektrale effektivitet, som er prsenteret i afhandlingen, vurderes at vretilstrkkeligt lovende m.h.p. yderligere overvejelser i forbindelse med fremtidige

OFDM baserede tradlse systemer.



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Acknowledgements

I am deeply indebted to my supervisor Prof. Ramjee Prasad, for giving me the

opportunity to work under his supervision and learn from his vast experience which

spans beyond technical fields. His continuous motivation helped me sail through

this voyage and reach the final destination. I express my gratitude to Elisabeth De

Carvalho who has been my co-supervisor for a major duration of my work and has

provided important feedback in several aspects of my work. I am also highly obliged

to Frank H.P. Fitzek and Ole Olsen who were my supervisors during the early part

of my research at Aalborg University without whose support I could not have begun

this work.

I am grateful to Dr. Sunil Sherlekar, for taking the initiative for this project

and to the management team of Tata Consultancy Services Ltd., India, for funding

this work. I specially thank Mr. Debasis Bandopadhay, for providing all support

needed to succesfully complete this work without any hindrance. I would like to

thank Mr. Arpan Pal, Mr. P. Balamuralidhar and Mr. Prateep Mishra, for giving

me the prestigious opportunity to work on this project. I have learnt a lot from my

team members of the Embedded Systems group of TCS Kolkata. I would like to

thank them all.

I would like to mention my colleague Imadur specially here for he has been

a continuous discussion partner throughout the work. I express my sincere thanks

to Daniel and Soren who have provided continuous help in understanding technical

concepts. I would like to remember Akhilesh, Basuki, Anas , Petar , Huan, Hiro with

whom I had the chance of some good technical discussion.

I am thankful to the several MSc. students who have made contributions to

this work while working on their graduation projects or on internships. Fuad, Bayu,

Carlos, Faisal and Nidcha have been associated with this work is some time or the

other. I like to specially cite Yuyuane Wang for his interest in research activities and

the contributions he made in many ways to this work.

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I thank all my colleagues and secretaries of section of RATE, CTIF, former

WING group and former Department of KOM, provided all necessary help needed to

do my research smoothly.Fleming B. Frederiksen was always keen about my progress and supported me

in several student projects. I am grateful to him for all his kindness. I would note the

help of Sanjay Kumar from BIT Mesra, Ranchi for providing important feedback to

make the final version of the thesis. Mrs. Nisha Gupta from BIT Mesra, also helped

in making the document complete. I am grateful to her for the help she extended.

I also owe a lot my colleague Nicola Marchetti for reading through the thesis and

helping to improve it.

It is the sacrifice and blessings of my parents that gave me the strength to

reach the completion of this work steadily. My sister kept continuously motivating

me to persevere to the end of this long project. It was the tremendous support from

my wife, Madhulipa, that helped me put all my effort in this work. She made many

sacrifices so that I could devote my time to this thesis. My little daughter Aane,

(Debosmita) has been a new inspiration in my life since she was born. Finally it is

by Gods grace that I could do my research relentlessly to complete the program in

time.



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xii CONTENTS

3.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.1.2 System Description . . . . . . . . . . . . . . . . . . . . . . . . 42

3.1.3 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . 453.1.4 Simulation Results and Discussion . . . . . . . . . . . . . . . . 47

3.1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

3.2 MC-SS with receiver impairments . . . . . . . . . . . . . . . . . . . . 57

3.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57


3.2.3 Simulation Environment, Results and Discussion . . . . . . . . 59

3.2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

Chapter 4 Bit loading on Pilot Sub Carriers 69

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4.2 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

4.3 Analytical Framework and Algorithm . . . . . . . . . . . . . . . . . . 76

4.4 Simulation and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 84

4.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Chapter 5 Adaptive Sub Carrier Bandwidth 915.1 Adaptive Sub Carrier Bandwidth in Time Division Multiplexing (TDM)-

OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92


5.1.2 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . 94

5.1.3 Algorithm for Adaptive Bandwidth for Sub Carriers . . . . . . 96

5.1.4 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 99

5.1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

5.2 OFDMA Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.2.1 Analytical Model . . . . . . . . . . . . . . . . . . . . . . . . . 108

5.2.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . 111

5.2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

5.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116

Chapter 6 Variable Guard Interval 119

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

6.2 System Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122

6.3 Required GI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122



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CONTENTS xiii

6.4 Performance and Discussion . . . . . . . . . . . . . . . . . . . . . . . 130

6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

Chapter 7 Hybrid Link Adaptation 135

7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

7.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

7.3 Hybrid LA strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

7.3.1 Different Link Adaptation Algorithms . . . . . . . . . . . . . . 140

7.3.2 LA with Different Sub-channel Sizes . . . . . . . . . . . . . . 143

7.3.3 Fixed Coding Rate . . . . . . . . . . . . . . . . . . . . . . . . 146

7.3.4 LA Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

7.3.5 Different LA & PC Rates . . . . . . . . . . . . . . . . . . . . 150

7.3.6 Interaction between Spatial Diversity and Link Adaptation . . 155

7.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158

7.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

Chapter 8 Link Adaptation under Transceiver Impairments 161

8.1 Influence of Non Linear High Power Amplifier (HPA) . . . . . . . . . 162

8.1.1 HPA Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164

8.1.2 Effect of HPA on different Modulation and coding rates . . . . 1668.1.3 Link Adaptation under HPA Impairments . . . . . . . . . . . 176

8.1.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187

8.2 LA under ICI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

8.2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189

8.2.2 LA under undetected ICI . . . . . . . . . . . . . . . . . . . . . 193

8.2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

8.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

Chapter 9 Conclusions and Future Work 199

9.1 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199

9.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202

Chapter A Selected Publications Related to the thesis 203

A.1 Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

A.1.1 IPR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

A.1.2 Journal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203

A.1.3 Conference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204c Suvra Sekhar Das, 2007


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xiv CONTENTS

A.2 Chapter wise Publications . . . . . . . . . . . . . . . . . . . . . . . . 206

Chapter B Link Adaptation 207

B.1 Bit and Power Loading Algorithm . . . . . . . . . . . . . . . . . . . . 207

Chapter C Hybrid Link Adaptation 213

Chapter D LA in OFDM systems under HPA 221

Bibliography 237

Vita 249



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List of Figures

1.1 Wireless Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1 Characterization of Fading . . . . . . . . . . . . . . . . . . . . . . . . 14

2.2 Propagation Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

2.3 Amplitude response . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4 channel impulse response and transfer function relationship . . . . . . 19

2.5 Multipath propagation . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2.6 A typical power delay profile . . . . . . . . . . . . . . . . . . . . . . . 21

2.7 Frequency domain channel response . . . . . . . . . . . . . . . . . . . 22

2.8 Power Spectral Density vs frequency of Jakes spectrum . . . . . . . . 24

2.9 Power Spectral Density vs frequency of typical Gauss spectrum . . . 24

2.10 Signal space diagram for rectangular 64-QAM . . . . . . . . . . . . . 24

2.11 Non orthogonal carriers . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.12 Orthogonal Sub carriers in Multi carrier systems (OFDM) . . . . . . 26

2.13 Time domain representation of the signal waveforms to show orthogo-

nality among the sub carriers . . . . . . . . . . . . . . . . . . . . . . 27

2.14 Base band modules of the OFDM transmitter . . . . . . . . . . . . . 28

2.15 Time Frequency representation of OFDM Signal . . . . . . . . . . . . 29

2.16 Frequency Selective and non Selective Fading . . . . . . . . . . . . . 30

2.17 The use of Cyclic Prefix . . . . . . . . . . . . . . . . . . . . . . . . . 30

2.18 Top level architecture of OFDM receiver circuitry showing important

signal processing modules in base band part. . . . . . . . . . . . . . . 31

2.19 Training sequence for WLAN [44] . . . . . . . . . . . . . . . . . . . . 31

2.20 SNR switching threshold points for LA System. M=4 indicates QPSK,

M=16 is 16-QAM, and M=64 is for 64-QAM, while C represents coding

rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.21 Link Adaptation basic framework . . . . . . . . . . . . . . . . . . . . 38

xv


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xvi LIST OF FIGURES

2.22 Time Sequence of Events in Link Adaptation . . . . . . . . . . . . . . 38

2.23 Spectral Efficiency Gain for LA System. . . . . . . . . . . . . . . . . 39

3.1 Transmitter for Sub-Carrier Hopped Multi Carrier Spread Spectrum

( S C H - M C - S S ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.2 Receiver for SCH-MC-SS . . . . . . . . . . . . . . . . . . . . . . . . . 44

3.3 Time frequency diagram of the sub carrier hopping scheme. It is shown

that a sub carrier allocated, which is represented by a particular colour

shade, moves in time and frequency grid in one packet. . . . . . . . . 44

3.4 Mean throughput Vs spreading gain at different SNRs with single sym-

bol detection, for channel model 2.

intr represents interleaved sub carrier arrangement and

blk implies block sub carrier arrangement.

OFDMA CDM is the parent MC-SS scheme, while SCH OFDMA CDM

is the proposed sub carrier hopping scheme. . . . . . . . . . . . . . . 48

3.5 10% outage throughput Vs spreading gain at different SNRs with single

symbol detection, for channel 2 . . . . . . . . . . . . . . . . . . . . . 49

3.6 Mean throughput Vs spreading gain at different SNRs with successive

interference cancelation, for channel 2 . . . . . . . . . . . . . . . . . . 50

3.7 10% outage throughput Vs spreading gain at different SNRs with suc-

cessive interference cancelation, for channel 2 . . . . . . . . . . . . . . 51

3.8 Mean throughput Vs spreading gain at different SNRs with single sym-

bol detection, for channel 6 . . . . . . . . . . . . . . . . . . . . . . . 52

3.9 10% outage throughput Vs spreading gain at different SNRs with single

symbol detection, for channel 6 . . . . . . . . . . . . . . . . . . . . . 53

3.10 Mean throughput Vs spreading gain at different SNRs with successive

interference cancelation, for channel 6 . . . . . . . . . . . . . . . . . . 54

3.11 10% outage throughput Vs spreading gain at different SNRs with suc-cessive interference cancelation, for channel 6 . . . . . . . . . . . . . . 55

3.12 Effect of different spreading gain for ideal receiver conditions. The

numbers in the bracket, e.g. MC-SS(4) indicates the spreading gain. . 60

3.13 Effect of loading for spreading gain of 16 under ideal conditions . . . 61

3.14 Effect of spreading gain in full load under residual carrier frequency

offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

3.15 Effect of loading for spreading gain of 16 under residual carrier fre-

quency offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62



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LIST OF FIGURES xvii

3.16 Effect of spreading gain in full load under Channel Estimation error . 63

3.17 Effect of loading for spreading gain of 16 under Channel estimation error 64

3.18 Effect of spreading gain on the 10% outage performance in full load forideal receiver conditions . . . . . . . . . . . . . . . . . . . . . . . . . 65

3.19 Effect of loading for spreading gain of 16 on the 10% outage perfor-

mance for ideal receiver conditions . . . . . . . . . . . . . . . . . . . 65

3.20 Effect of spreading gain on the 10% outage performance in full load for

Channel Estimation error and synchronization error . . . . . . . . . . 66

3.21 Effect of loading for spreading gain of 16 on the 10% outage perfor-

mance for Channel Estimation error and synchronization error . . . . 66

4.1 OFDM Symbol Format . . . . . . . . . . . . . . . . . . . . . . . . . . 724.2 16-QAM Data constellation . . . . . . . . . . . . . . . . . . . . . . . 74

4.3 QPSK Pilot constellation . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.4 Piece-wise linear interpolation . . . . . . . . . . . . . . . . . . . . . . 82

4.5 BER Vs SNR, Data64-QAM, pilotBPSK.alg3 completely data aided pilot based OFDM system, using theexact algorithm.

alg2 the exact algorithm in the proposed semiblind environment.

alg1 the proposed low complexity, approximated algorithm. . . . . 854.6 BER Vs SNR, Data64-QAM, pilotQPSK . . . . . . . . . . . . . 854.7 BER Vs SNR, Data16-QAM, pilotBPSK . . . . . . . . . . . . . 864.8 BER Vs SNR, Data16-QAM, pilotQPSK . . . . . . . . . . . . . 874.9 BER Vs SNR, DataQPSK, pilotBPSK . . . . . . . . . . . . . . . 87

5.1 Time frequency diagram for the proposed TDM based ASB OFDM . 93

5.2 Time frequency diagram of the proposed Adaptive Sub Carrier Bandwidth

(ASB) with Band Division Multiplexing (BDM) OFDM . . . . . . . . 94

5.3 SINR vs sub carrier bandwidth at 15dB SNR . . . . . . . . . . . . . 96

5.4 Throughput vs sub carrier bandwidth at 15dB SNR . . . . . . . . . . 97

5.5 Sub carrier bandwidth selected by the proposed ASB system. . . . . . 100

5.6 Throughput comparison of the proposed ASB vs standard Fixed Sub

Carrier Bandwidth (FSB) OFDM systems, at 15 dB SNR. . . . . . . 101

5.7 Throughput comparison of the proposed ASB vs FSB OFDM systems,

at 25 dB SNR. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

5.8 BER of the proposed ASB and FSB OFDM systems at 15 dB SNR,

when target BER is 0.01 . . . . . . . . . . . . . . . . . . . . . . . . . 102c Suvra Sekhar Das, 2007


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xviii LIST OF FIGURES

5.9 Frequency domain configuration of Variable Sub Carrier Bandwidth

(VSB) OFDM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5.10 Downlink Transmitter for the proposed VSB Orthogonal FrequencyDivision Multiple Access (OFDMA) at the base station . . . . . . . . 106

5.11 Up link Receiver for the proposed VSB OFDMA at the base station . 107

5.12 SINR of standard OFDM systems at 20dB SNR . . . . . . . . . . . . 113

5.13 Capacity of standard OFDM systems at 20dB . . . . . . . . . . . . . 114

5.14 Capacity of standard OFDM systems at 10dB . . . . . . . . . . . . . 114

5.15 Capacity of VSB OFDM at 20dB . . . . . . . . . . . . . . . . . . . . 115

5.16 Capacity comparison when users with different mobility conditions co-

exist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

6.1 Effect of small GI. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

6.2 Interference Power due to previous OFDM symbol vs Ratio of GI over

rms del ay spread. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

6.3 SINR vs Ratio of GI over rms delay spread . . . . . . . . . . . . . . . 129

6.4 Ratio of GI Vs rms delay spread. Depicting variation of Tgi with

respect to various . . . . . . . . . . . . . . . . . . . . . . . . . . . 132

6.5 Performance with increasing SNR. . . . . . . . . . . . . . . . . . . . . 133

6.6 Cumulative Distribution Function of required GI. . . . . . . . . . . . 1336.7 Gain in throughput of the proposed VGI over fixed GI system for 2dB

extra SNR for 1s rms delay spread. . . . . . . . . . . . . . . . . . . 134

7.1 OFDM based link adaptation transceiver architecture . . . . . . . . . 138

7.2 Link adaptation frame structure. . . . . . . . . . . . . . . . . . . . . 139

7.3 Spectral Efficiency for SISO with Fd=50Hz, rms = 0.5s . . . . . . . 140

7.4 Power Utilization for SISO with Fd=50Hz, rms = 0.5s . . . . . . . . 141

7.5 Spectral Efficiency for SISO with Fd=250Hz, rms = 2s . . . . . . . 141

7.6 Power Utilization for SISO with Fd=250Hz, rms = 2s . . . . . . . . 142

7.7 Spectral Efficiency with Doppler 50Hz, delay spread 0.5s . . . . . . 144

7.8 Spectral Efficiency with Doppler 250Hz, delay spread 2s . . . . . . . 145

7.9 Block Error Rate (BLER) for SISO,LA per 1 frame(s),BLER=0.05,

rms=0.5s, fd=50Hz. . . . . . . . . . . . . . . . . . . . . . . . . . . 146







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LIST OF FIGURES xix

7.14 Combined slow LA with fast power control . . . . . . . . . . . . . . . 151

7.15 Spectral Efficiency for Different LA & PC Rates, Fd=50Hz,rms = 0.5s153

7.16 Spectral Efficiency for Different LA & PC Rates, fd=250Hz, rms = 2s 1547.17 Spectral Efficiency for Multi-antenna Schemes, LA Per 0.5ms . . . . . 155

7.18 Spectral Efficiency for Multi-antenna Schemes, LA Per 2ms . . . . . 156



8.1 Power Back of (BO) representation in Rapps Model . . . . . . . . . . 165

8.2 Relation between Amplifier Distortion and BO (in dB) . . . . . . . . 166

8.3 Spectrum plot of OFDM signal. BO indicates BO value in dB. . . . 167

8.4 SDNR plot for 4QAM modulation in AWGN Channel. . . . . . . . . 169

8.5 SDNR plot for 4QAM modulation in Fading Channel. The BO values

are given in dB. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169

8.6 BER vs SNR curve for uncoded and M=4 in fading channel . . . . . 172

8.7 BLER vs SNR curve for C = 12

and M=4 in fading channel . . . . . . 172

8.8 BER vs SNR curve for uncoded and M=16 in Fading channel . . . . 173

8.9 BLER vs SNR curve for C = 12

and M=16 in fading channel . . . . . 173

8.10 BER vs SNR curve for uncoded and M=64 in Fading channel . . . . 174

8.11 BLER vs SNR curve for C = 12 and M=64 in fading channel . . . . . 174

8.12 TD plot for F EC = 12

with BLER Threshold= 0.1 in Fading Channel 175

8.13 TD plot for F EC = 12


8.14 Flow chart of bit loading algorithm used for analyzing the influence of

HPA in LA OFDM systems . . . . . . . . . . . . . . . . . . . . . . . 177

8.15 PAPR distribution for LA based OFDM system. . . . . . . . . . . . . 179

8.16 Performance of LA system with basic LUT when no power amplifier

is applied. HPA-0 implies no HPA situation while HPA-1 implies that

HPA is used in the simulation. . . . . . . . . . . . . . . . . . . . . . . 180

8.17 Performance of LA system with basic LUT when power amplifier is used181

8.18 Performance of LA system with revised LUT when power amplifier is

used . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

8.19 Spectral Efficiency comparison for LA system with and without PAPR

consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183

8.20 Performance of LA system with basic LUT for 6 dB of BO power . . 183

8.21 Performance of LA system with basic LUT for 4 dB of BO power . . 184

8.22 Performance of LA system with revised LUT for 6 dB of BO power . 186c Suvra Sekhar Das, 2007


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xx LIST OF FIGURES

8.23 Performance of LA system with revised LUT for 4 dB of BO power . 186

8.24 Impact of frequency offset on 4-QAM in fading channel. . . . . . . . . 190

8.25 Impact of frequency offset on 4-QAM, FEC rate 1/2, in fading channel. 1908.26 Impact of frequency offset on 16-QAM in fading channel. . . . . . . . 191

8.27 Impact of frequency offset on 16-QAM, FEC rate 1/2, in fading channel.191

8.28 Impact of frequency offset on 64-QAM in fading channel. . . . . . . . 192

8.29 Impact of frequency offset on 64-QAM, FEC rate 1/2, in fading channel.192

8.30 Bler performance without additional margin for coding rate 1/2 . . . 195

8.31 Bler performance with additional margin for coding rate 1/2 . . . . . 195

8.32 Spectral efficiency performance with additional margin for coding rate

1/2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196

B.1 Flow diagram of the SAMPDA algorithm . . . . . . . . . . . . . . . . 208

B.2 Spectral efficiency achievement of the adaptation algorithms . . . . . 211

B.3 Number of iterations required by different adaptation algorithms . . . 211

C.1 Throughput comparison of different Link adaptation algorithms at 0.5

s and 2.0 s rms delay spread and Doppler condition for sub-band

size of 8 sub carriers . . . . . . . . . . . . . . . . . . . . . . . . . . . 214

C.2 Power utilization comparison of different Link adaptation algorithmsat different rms delay spread and Doppler condition for sub-band size

of 8 and 32 sub carriers . . . . . . . . . . . . . . . . . . . . . . . . . . 215

C.3 Throughput performance of different sub-band sizes for different rms

delay spread, Doppler velocity. . . . . . . . . . . . . . . . . . . . . . . 216

C.4 Throughput performance comparison for fixed coding with adaptive

modulation Vs adaptive modulation and coding for sub-band size of 8

sub carriers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217

C.5 Throughput comparison for different adaptation rates, for rms delay

spread of 0.5s at 20 kmph . . . . . . . . . . . . . . . . . . . . . . . . 219

D.1 Comparison of theoretical and simulated CDF of PAPR . . . . . . . . 223

D.2 Effect of BO of 6 dB on 16QAM constellation points . . . . . . . . . 223

D.3 Effect of BO on 16QAM constellation points . . . . . . . . . . . . . . 223

D.4 16QAM basic constellation points . . . . . . . . . . . . . . . . . . . . 224

D.5 Effect of different modulation scheme on CDF of PAPR when Fast

Fourier Transform (FFT) size is 512. M indicates the modulation level,

and C the coding rate. . . . . . . . . . . . . . . . . . . . . . . . . . . 224



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LIST OF FIGURES xxi

D.6 Effect of different coding rate on the CDF of PAPR when FFT size is

128. M indicates the modulation level, and C the coding rate. . . . . 224

D.7 SDNR plot for 16QAM modulation in AWGN Channel . . . . . . . . 225D.8 SDNR plot for 16QAM modulation in Fading Channel . . . . . . . . 225

D.9 SDNR plot for 64QAM modulation in AWGN Channel . . . . . . . . 225

D.10 SDNR plot for 64QAM modulation in Fading Channel . . . . . . . . 225

D.11 BER vs SNR curve for uncoded and M=4 in AWGN channel . . . . . 226

D.12 BLER vs SNR curve for C = 12

and M=4 in AWGN channel . . . . . 226

D.13 BER vs SNR curve for uncoded and M=16 in AWGN channel . . . . 226



D.15 BER vs SNR curve for uncoded and M=64 in AWGN channel . . . . 226















D.23 TD plot for F EC = 12

with BLER Threshold= 0.1 in AWGN . . . . . 228

D.24 TD plot for F EC = 12 with BLER Threshold= 0.05 in AWGN . . . . 228D.25 TD plot for F EC = 1

3with BLER Threshold= 0.1 in AWGN . . . . . 228


with BLER Threshold= 0.05 in AWGN . . . . 228






with BLER Threshold= 0.1 in AWGN . . . . . 229


with BLER Threshold= 0.05 in AWGN . . . . 229



D.32 TD plot for F EC =2

3 with BLER Threshold= 0.05 in Fading Channel 229

c Suvra Sekhar Das, 2007


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List of Tables

1.1 Wireless Systems using OFDM . . . . . . . . . . . . . . . . . . . . . 2

2.1 Value of parameters for urban terrain. . . . . . . . . . . . . . . . . . 17

2.2 Parameters in WLAN . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.3 WMAN system parameters . . . . . . . . . . . . . . . . . . . . . . . . 34

2.4 Switching Threshold for Link Adaptation . . . . . . . . . . . . . . . . 39

4.1 SNR advantage of pilots in different modulation schemes . . . . . . . 73

4.2 Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4.3 Semi Blind Configurations and gains . . . . . . . . . . . . . . . . . . 88

5.1 List of Symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

6.1 Bit Error Rate for SINR: 15 dB , rms delay spread: 1 s, Carrier

Frequency: 3.5 GHz, Bandwidth 20 Mhz bandwidth, Number of sub

carriers: 1024, Guard length for fixed GI: 128. . . . . . . . . . . . . . 131

7.1 System Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

7.2 SISO,LA per 1 frame(s),BLER=0.05, taurms=0.5s,fd=50Hz . . . . . 145

7.3 SNR Threshold for Coding Rate Switching . . . . . . . . . . . . . . . 150

7.4 Overhead in Mbps for Adapt Power LA . . . . . . . . . . . . . . . . . 155

7.5 Summary of Hyrbid Link Adaptation . . . . . . . . . . . . . . . . . . 159

8.1 Table for Calculation of Total Degradation in dB . . . . . . . . . . . 175

8.2 Variation in number of sub carriers Vs SNR. . . . . . . . . . . . . . 178

8.3 LUT with basic and updated values for system with FEC= 12

in AWGN

Channel(Values in dB) . . . . . . . . . . . . . . . . . . . . . . . . . . 179

8.4 LUT with reference values for system with FEC = 12

in Fading Chan-

nel (V al ues i n dB ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182

xxiii


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1Introduction

1.1 Background to OFDMOrthogonal Frequency Division Multiplexing (OFDM) based access/multiplexing schemes

are used in wireless applications such as Wireless Personal Area Network (WPAN),

Wireless Local Area Network (WLAN), Wireless Metropolitan Area Network (WMAN),

high quality digital radio (Digital Audio Broadcasting (DAB)) and television broad-

casting (Digital Video Broadcasting (DVB)) [1]. It is being considered for IEEE 802.20,

IEEE 802.16 and 3GPP-LTE [2] systems. OFDM will remain as the key enabling

technology for achieving higher data rates in wireless packet based communication

systems in the next few years to come [3]. Its extensions with time frequency domain

spreading are under investigation for use in future wireless systems [4]. OFDM tackles

the frequency selective wireless fading channel effectively by converting a wide band

signal into a set of parallel narrow band signals such that each stream of narrow band

signal experiences flat fading. With the use of cyclic prefix to eliminate Inter Symbol

Interference (ISI), there is need for only a simple one tap equalizer at the OFDM re-

ceiver. OFDM brings in unparalleled gains in bandwidth savings, which leads to very

high spectral efficiency. These properties make OFDM systems extremely attractive

transmission technologies for wireless scenarios.

1


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2 Chapter 1. Introduction

OFDM was initially used in military systems, such as KINEPLEX in 1958,

KATHRYN in 1967, and ANDEFT in 1968 [5]. A bank of conventional transmit-

ters/receivers with overlapping spectra were used in these systems. In 1971, Wein-stein and Eberts proposal to use the Discrete Fourier Transform (DFT) to modu-

late/demodulate all the sub-carriers, with a single oscillator [6] was a pioneering effort.

With its implementation via FFT finally OFDM was realizable in commercial commu-

nication system. It started with a number of wireline standards. High bit-rate Dig-

ital Subcarrier Lines (HDSL) [7], Asynchronous Digital Subscriber Line (ADSL)[8],

and Very High speed Digital Subscriber Line (VDSL)[8] were a sequence of standards

which led to throughput of up to 100Mb/s. Then it was introduced into the wireless

arena through DAB[9] and WLAN [10, 11]. Then came DVB[12, 13] around 2004.

In the WMAN application, OFDM is considered for the Worldwide Interoperability

for Microwave Access (WiMAX) implementation via the IEEE 802.16d,a,e [14, 15]

standards. It is also being considered for the 3GPP Long Term Evolution, which in

under development.

Table 1.1 summarizes some wireless systems which use OFDM as the trans-

mission technology [1].

Table 1.1: Wireless Systems using OFDM

Application WMAN WLAN WPAN

Cell Radius 1km to 20km up to 300m few 10s of meterMobility High and low Low very low

Freq Band 2-66Ghz 2-5Ghz 5-10GHzData Rate Few Mbps upto 100Mbps upto 10 Mbps

Deployment IEEE 802.16a, d, e,WiMAX, 3GPP-LTE

IEEE 802.11a, g,HiperLAN2

IEEE 802.15,ZigBee

1.2 Motivation

Now is the juncture where wireless internet access is taking over wire line internet

access in several countries. Edinholm, who was the chief technology officer of Nortel

Networks, predicted the exponential growth of data rate in wireline and wireless

networks [16] and said that wireless data access would eventually catch up with it

wireline counterpart but not within 2008. However, the current scenario of Mobile

users is tending to shorten the time line.



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1.2 Motivation 3

It is said that currently there are around 2.7 billion mobile phone users. The

use of mobile phones is changing the way of life for the next generation, which has

already been significantly changed by the Internet.In Japan, South Korea and China,the majority of web access now comes from mobile phones, not Personal Computers

(PCs). The need to support higher and higher data rate in wireless systems can be

easily understood in this context.

Fig. 1.1 represents a common view of current and future wireless systems. Only

two dimensions are present in this picture. Power consumption is also an important

dimension along with these metrics. In current systems, there is a tradeoff between

mobility, coverage and data rate. The need for next generation systems is to provide

higher data rate at high mobility conditions, but at the same time implementation

complexity of the devices must be as minimum as possible to reduce power consump-

tion. The systems must also be able to cater to a whole range of mobility conditions,

and must consider that devices with different capabilities will coexist in the same

network. In other words future generation systems must be able to provide higher

data rates at all mobility conditions consuming minimum power and other available

resources.

This is supported by the visionary statement It is dangerous to put limits on

wireless data rates, considering economic constraints, by Professor Ramjee Prasad

in 1999 [1]. Wireless spectrum available for commercial use is limited and expensive.

One of the main ways to support the increasing demand of wireless data services is

to push spectral efficiency to its limits.

Increasing the spectral efficiency of wireless communication systems is one of

the greatest challenges faced by wireless communication engineers. The available

bandwidth is scarce and costly, where as, there is a huge demand for data rate cre-

ated by increasing number of subscribers and increase in multimedia application which

require large bandwidth. Increasing the spectral efficiency is the answer to this grow-

ing demand of data rate when the available bandwidth is limited. OFDM already

provides very high spectral efficiency but current implementations of OFDM do not

fully exploit the capabilities of OFDM. There are still several avenues which can be

explored to increase the spectral efficiency of OFDM systems even further. Therefore

the necessity to increase the spectral efficiency has been a prime motivating factor

for this work.

One of the approaches to increase the spectral efficiency is to design high per-

formance receivers, which leads to increase in signal processing complexity. Increase

in receiver complexity contributes to higher power consumption, and costlier compo-c Suvra Sekhar Das, 2007


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Fast

Medium

Slow

Moveable

Stationary

UserMobility

TransmissionRate(Mbits/s)

10.0 100.01.00.1

3G

WLAN802.11a/gHiperlan/2MMAC

2000

4G

2010

Mobile

WiMAX

Fixed

WiMAX

3GPP-LTE

WiBro

Figure 1.1: Wireless Systems

nents. With increase in multimedia applications, there is a further addition to theprocessing complexity. With limited battery power available on portable devices to

support the full range of operations for long durations the need for low complexity low

power consuming algorithms can be easily understood. To target the mass market of

wireless modules, low cost solutions have to be found, keeping in mind the tradeoff

between efficiency and price. Therefore it is important that the techniques to improve

spectral efficiency do not increase the signal processing complexity. Hence one of the

motivating factors for this work is the need for low complexity schemes to increase

the spectral efficiency of wireless systems.

1.3 Problem Definition

It is known that OFDM is spectrally very efficient and robust in dealing with the

frequency selective wireless fading channels, yet some combinations of spread spec-

trum techniques and OFDM are being considered to develop even better systems [17].

Though some works give details of their performance, it is important to compare them

against OFDM in the same test conditions, which include among others non ideal re-



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1.3 Problem Definition 5

ceiver operations. Thus the first problem addressed is the performance evaluations

and enhancements to multi carrier spread spectrum techniques for indoor conditions.

This analysis has been extended to the conditions when the receiver cannot obtainperfect frequency synchronization and there exists channel estimation error.

Though OFDM brings several benefits, yet its performance depends on the

channel estimation accuracy and residual phase error due to remaining uncorrected

carrier frequency offsets [18]. Therefore pilot sub carriers are embedded among data

sub carriers [11] so that good estimates of these errors can be obtained. Good es-

timation of the phase errors helps in better compensation of the errors which leads

to improved performance. Since pilot sub carriers do not carry information bits they

are overhead and causes loss in bandwidth efficiency. Investigation of methods of

reducing this loss in bandwidth efficiency to improve the spectral efficiency of the

system is thus considered as a problem area in this thesis.

One of the strengths of OFDM is its closely packed sub carriers. The sub

carrier bandwidth is a primary design parameter in OFDM systems. Carrier fre-

quency offset and Doppler frequency spread cause ICI which severely degrade the

performance of OFDM based systems. There are several algorithms to estimate and

compensate the carrier offset [19]. However, the Doppler frequency spread consistsof multiple frequency offsets [20] and cannot be compensated by carrier offset com-

pensation algorithms. Algorithms to address ICI due to Doppler are very complex

from implementation point of view [21, 22]. The ratio of the maximum uncorrectable

residual carrier offset and the maximum Doppler frequency spread to the sub carrier

spacing is an important factor on which the ICI depends. The maximum value of the

ratio is usually kept within 2% [23]. Thus once the maximum distortion is known

the maximum value of sub carrier bandwidth gets decided. The larger the maximum

distortions, the larger is the value of the sub carrier bandwidth needed in order to

keep the ratio within the limits mentioned above. The useful signal duration of the

symbol is inversely proportional to the sub carrier spacing. Therefore the larger the

sub carrier spacing the smaller is the useful signal duration. The OFDM symbol

efficiency can be defined as

OFDM symbol efficiency =useful symbol duration

total symbol duration

=useful symbol duration

useful symbol duration + guard interval duration

(1.1)c Suvra Sekhar Das, 2007


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The GI duration is dependent on the Root Mean Square (RMS) delay spread of the

channel and not on the residual carrier offset or the maximum Doppler frequency

spread. Therefore the design of GI length is independent of the sub carrier band-width value. Thus the OFDM symbol efficiency decreases as the useful signal dura-

tion reduces, due to increasing sub carrier bandwidth, when the GI duration is fixed.

This design is based on the worst case scenario which is not always encountered. In

other situations there is a large margin in the parameter and there is an unnecessary

wastage of resources. Thus one of the problems addressed in this thesis is to inves-

tigate adaptive techniques to overcome this situation and improve upon the spectral

efficiency without increasing the complexity of the User Equipment (UE).

The GI is an important designed parameter for OFDM systems. Its contri-

bution to the overhead has already been seen in (1.1). A properly chosen length of

GI prevents ISI and ICI which enables the use of single tap equalizer per sub car-

rier. Ideally the length of GI should be larger than the maximum excess delay of the

channel. Now similar to the earlier situation, the operating conditions are not that

harsh in most occasions. In such scenarios there is wastage of power and bandwidth.

If the margins were reduced, then performance would be affected since there would

be introduction of irreducible ICI and ISI when worse conditions are encountered.

Therefore several attempts to address the situation have been made towards develop-

ing high performance receivers, which can reduce the interference but these schemes

unfortunately need heavy signal processing [24],[25]. Therefore a technique is pro-

posed in this work, which can reduce the GI overhead, yet does not incur penalty in

terms of performance or receiver complexity.

Link Adaptation (LA) through the use of adaptive modulation, coding and

power control has been under active investigation to overcome the time frequency

selective fading of the wireless channel in an effective manner using feedback of channel

state information from the receiver [26]. LA becomes more complex when applied

to multi carrier systems because the degrees of freedom available for performance

improvement increase. It is vital to investigate schemes with low implementation

complexity. It may not be necessary to use all of the degrees of freedom at the same

time. Some aspects of reduced LA rate are investigated in [27], however, a much wider

scenario and several levels of adaptation are analyzed in this thesis. The tradeoff with

performance loss in such mechanisms needs to be identified which is also addressed

in this thesis.

Though LA schemes promise significant gains for OFDM based systems, the

performance is limited by the handicaps of OFDM. There have been investigations



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1.4 Goal and Scope of the thesis 7

verifying the performance of LA due to channel feedback delay and channel estimation

error [28]. However, their tolerance to synchronization error and non linear operation

of the power amplifier, which becomes significant for OFDM with high Peak to Av-erage Power Ratio (PAPR) properties, need a critical study. Hence finding the true

performance of OFDM systems using LA under such conditions is a prime area of

investigation of this thesis.

1.4 Goal and Scope of the thesis

This project started as a collaboration between Tata Consultancy Services, India and

Aalborg University. The objective of this project is to investigate efficient techniquefor broad band wireless communication system for indoor and outdoor scenarios. The

first goal towards the objective is to find a suitable multiplexing scheme among OFDM

and MC-SS systems. Therefore in order to make a high efficiency system the second

goal is to find transmission mechanisms to overcome the impairments which impede

OFDM based schemes. The research focus is in the physical layer.

The environments under consideration are both indoor and outdoor scenarios.

Such conditions, would cover WLAN and WMAN applications. Under indoor and low

mobility conditions, physical specifications from IEEE 802.11a,g [11] based WLAN

standard have been considered for simulations. The parameters for these include a

bandwidth of 5 MHz - 20 MHz, with a carrier frequency in the range of 2GHz to

6GHz as per the situation. The channel model used for multi path propagation is

from [29]. For outdoor conditions, physical layer system parameters closely adhere

to the WIMAX and the developing 3GPP LTE [2] specifications, where bandwidth is

5MHz.

1.5 Research MethodologyThe impairments that affect general OFDM systems are first identified. The problems

have been evaluated in specific scenarios where they are more prevalent. Each scenario

usually maps to either WLAN or WMAN. Accordingly either 802.11a/g parameters

or WiMAX/3GPP-LTE parameters have been considered in respective situations.

The analysis presented in this work consists of analytical as well as computer

simulation. The system model is usually built on an analytical framework. The

appropriate channel conditions are simulated following standard channel models such

as [29],[2],[30],[31],[32], etc as applicable. Analysis is made for uncoded systems andc Suvra Sekhar Das, 2007


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also considering forward error control coding. As found suitable, capacity analysis is

also done to find the potential of a scheme. Key performance indicators have been

Bit Error Rate (BER), Block Error Rate (BLER), Frame Error Rate (FER), spectralefficiency and outage in different situations.

1.6 Organization of the thesis

Chapter 2: This chapter presents the technical background and introduction to

multi carrier techniques needed to explain the work in the following chapters.

It details the fundamental description of standard OFDM systems and explains

important concepts of multi carrier systems, such as orthogonality of sub car-riers and the use of cyclic prefix among others. It also describes the wireless

channel model used in the analysis.

Chapter 3: The analysis of MC-SS systems is provided, and the necessity of sub

carrier hopping for indoor conditions is evaluated in this chapter. The effect

of varying spreading gain, and symbol loading in indoor conditions is evalu-

ated here. The importance of using Successive Interference Cancelation (SIC)

receivers under such conditions is also verified. The performance comparison

of MC-SS against basic OFDM system under ICI and channel estimation error

conditions is also included in the chapter.

Chapter 4 In this chapter, the method of using semi blind pilot sub carriers to en-

hance spectral efficiency of OFDM systems in WLAN environment is presented.

It brings out the possible option of loading the pilot sub carriers with informa-

tion bits without degrading bit error rate performance of the system. It also

discusses a low complexity residual phase tracking algorithm for implementing

the scheme.

Chapter 5 This chapter proposes a novel mechanism to deal with inter carrier inter-

ference generated due to Doppler effect and carrier frequency offset. Instead of

using a compensation mechanism, it proposes adaptive sub carrier bandwidth to

dynamically minimize the impact of Doppler. The chapter consists of two parts,

where the first part deals with evaluating the system with Orthogonal Frequency

Division Multiplexing - Band Division Multiplexing (OFDM-BDM) and Orthog-

onal Frequency Division Multiplexing - Time Division Multiplexing (OFDM-

TDM) systems. The second part of the chapter presents capacity analysis for



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1.7 Contributions of this Thesis 9

a proposed model of Orthogonal Frequency Division Multiple Access - Time

Division Multiple Access (OFDMA-TDMA) systems which use ASB.

Chapter 6 This chapter contains the proposal of using adaptive guard interval to

reduce the overhead of GI. The algorithm presented here selects the optimal

GI length based on the channel condition, residual carrier offset, required SNR

margin and received SNR condition. The analysis can be applied to both WLAN

and WMAN scenario.

Chapter 7 This chapter presents the proposal of hybrid link adaptation mechanism

in OFDM system for spectral efficiency enhancement. Link adaptation dis-

cussed in this chapter includes dynamic selection of modulation, coding, andpower for the transmitted symbols. The impact of channel condition on the

selection of sub band size, modulation and coding rate adaptation interval are

investigated. It presents the performance of a proposed low complex system

using fast power adaptation along with slow modulation adaptation. Detailed

performance analysis is presented which provides guidelines for implementation

in a typical outdoor environment.

Chapter 8 Investigation of LA system in presence of non linear distortion due to

the HPA and the effect of ICI is made in this chapter. Method to overcome

these impairments is also discussed in this chapter.

Chapter 9 This is the concluding chapter of the thesis which summarizes the con-

clusions of each contributing chapters and lists the possible future works.

1.7 Contributions of this Thesis

1. Multi Carrier Spread Spectrum (MC-SS) techniques enhance the performance

of OFDM systems by increasing the frequency diversity gain through spreading

over frequency domain. However, for indoor conditions where the coherence

bandwidth and coherence time are quite large, then a set of sub carriers will

remain in deep fade for a long duration. In such a situation the proposal for

Sub-Carrier Hopped Multi Carrier Spread Spectrum (SCH-MC-SS) for indoor

systems is made in this work. The proposed SCH-MC-SS can improve the

outage performance for indoor scenarios by increasing diversity using fast sub

carrier frequency hopping over the entire system bandwidth.c Suvra Sekhar Das, 2007


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2. Performance evaluation and comparison of OFDM and MC-SS for various prac-

tical operating conditions such as channel estimation error and frequency syn-

chronization errors have been made. These analysis bring out the details ofperformance difference between OFDM and MC-SS. This helps in selecting the

best multiplexing scheme in different situations. For this project since increas-

ing the spectral efficiency is a major target, OFDM stands out as the clear

winner.

3. Pilot sub carriers used for residual phase tracking in WLAN type systems carry

pre-defined symbols and hence are overhead. Semi blind pilots have been pro-

posed which can carry data symbols and can still be used for estimating the

phase rotation due to the high SNR content of these sub carriers. This technique

of overloading the pilot sub carriers increase the spectral efficiency by 5-15% by

reducing the pilot overhead in IEEE 802.11a/g type WLAN environment.

4. A low complexity algorithm for implementing the semi blind pilots is presented

in this thesis. The low complexity algorithm does not compute the phase angle

using the highly complex inverse tangent function. Instead it uses the com-

plex coefficients directly for estimating the phase coefficients and uses them for

compensation.

5. Proposal for Adaptive Sub carrier Bandwidth (ASB) to overcome ICI due to

Doppler frequency spread in Orthogonal Frequency Division Multiplexing -

Time Division Multiple Access (OFDM-TDMA) systems is made in this work.

6. Proposal for adaptive sub carrier bandwidth to overcome ICI due to Doppler

frequency spread in OFDMA systems is made in this thesis. The improvement

in spectral efficiency obtained from these schemes is up to 30%.

7. Algorithm for dynamically variable Guard Interval (GI) is developed which

reduces the GI overhead up to 60% and contributes towards spectral efficiency

enhancement by as much as 15%.

8. Proposal for hybrid link adaptation to suitably use the many degrees of free-

dom available for link adaptation by combining fast and slow adaptation of

the different parameters so as to gain high efficiency while using less complex

system is made. The feedback overhead is reduced up to 50% by the proposed

techniques. Though primarily parameters from 3GPP LTE have been used, an



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1.7 Contributions of this Thesis 11

investigation of these parameters has been made which provides important in-

sights into possible alternative to the ones selected till now. These results can

thus serve as important inputs to future wireless systems.

9. Problem analysis and solution for implementing Link Adaptation (LA)-OFDM

under the effect of non linear distortions introduced by High Power Amplifier

(HPA) is made in this work.

10. Analysis and solution for implementing LA under practical non ideal conditions

of Inter Carrier Interference (ICI) due to Doppler frequency spread is also made.



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2.1 Wireless Channel 15

Large Scale Fading is dealt by propagation model that predicts the mean received

signal strength for an arbitrary transmitter receiver separation. The large scale fading

model gives such an average with measurements across 4 to 40 [34], where is thewavelength. This is useful for estimating coverage area. Large Scale fading can be

broadly classified as path loss and shadowing. Path loss deals with the propagation

loss due to distance between transmitter and receiver while shadowing describes vari-

ation in the average signal strength due to varying environmental clutter at different

locations.

Small Scale Fading deals with signal strength characteristics within small distance

of the receiver location. In such region of space the average signal strength remains

constant. Multi path propagation of the electromagnetic waves is the main cause

of such effects. It includes the effect of time, space and frequency selective fading

characteristics. For each domain, there are broadly two kinds of conditions, one is

when the variability is high and the other when the variability is very small over the

observation interval.

Thus, the signal strength at a particular location depends on the large scale

fading and the small scale fading. As the receiver moves, the instantaneous power of

the received signal varies rapidly giving rise to small scale fading. In such a situation

the received power may vary by as much as 20-40dB over a range a few order of afraction of a wavelength depending upon the particular environment. As the distance

between the Transmitter and the Receiver increases, the local averaging of the received

signal power decreases gradually, this is predicted by the large scale fading statistics.

This phenomenon of a combined slow and fast fading is briefly explained in Fig. 2.2.

Power

Distance

PathLossShadowing

SmallScaleFastFading

Figure 2.2: Propagation Loss

Three main factors which influence the radio wave propagation are Reflection,c Suvra Sekhar Das, 2007


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16 Chapter 2. Wireless Channel and Multi Carrier Systems

Diffraction and Scattering. Reflection is caused when the Electromagnetic Waves

(EM) impinge upon surface having dimensions much larger than the wavelength of

the impinging wave. Diffraction is caused due to effects of sharp edges in the pathof the radio waves between the Transmitter and the Receiver. Scattering is caused

when the EM waves encounter objects of dimension much smaller than the wave in

the propagation medium.

Most radio propagation models use a combination of empirical and analytical

methods. The empirical approach is based on fitting curves or analytical expressions

that recreate a set of measured data. This has the advantage of implicitly taking into

account all the propagation factors. However the validity of an empirical model at

transmitter frequencies or environments other than those used to derive the model

can only be established by additional measured data in the new environment and

frequencies. Propagation models and multi path reflection models have emerged over

time to enable easy simulation of the wireless channel [34].

2.1.2 Propagation loss

There are many models for predicting the path loss such as Hata-Okumura and

COST231-Hata model [34]. However, both these models are for frequency ranges

up to 2000 MHz. To modify this a model is proposed in [30]. For a given close indistance1, d0 of 100m, the median path loss (PL), in dB, is given by (2.1) [30].

PL = A + 10nplog10d

do+ s d > do, (2.1)

where

A = 20log10(4do/), (2.2)

and np is the path loss exponent and is given by,

np = a bhb + c/hb, 10m < hb < 80m. (2.3)

The value of a, b and c are different for different terrain types. Values for urban

area are given in Table 2.1 [32]. The shadowing factor is s and follows a log normal

1The Friis free space model, which is the basis for large scale propagation models, is valid forvalues of d which are in the far field of the transmitting antenna, i.e. does not hold for d = 0.Therefore, large-scale propagation models [34] use a close-distance, d0, as known as received powerreference point. The received power, Pr(d), at any distance d d0 may be calculated in relationto that received at d0. The reference distance is chosen so that it lies in the distance used in the

mobile communication system.



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Table 2.1: Value of parameters for urban terrain.

Parameters Urban terrain Unit

a 4.6b 0.0075 m1

c 12.6 m

distribution, with a typical value around 6 dB [30]. This model is proposed for a

receiver antenna height of 2 m and operating frequency of 2 GHz, and a correction

factor for other frequencies and antenna heights is proposed [32]. The modified path

loss in (2.1) is:

PLmod = PL + PLf + PLh, (2.4)

where, PL is path loss given by (2.1), PL f is frequency correction term given by

6log10(f/2000), f is the frequency in MHz and PLh is receiver antenna height cor-

rection term given by 10.8log10(h/2), where h is the new receiver antenna height(m) such that 2 < h < 8.

The propagation model used for 3GPP-LTE system can be found in [2] where

different parameters have been used for different channel condition and cell orienta-

tion.

2.1.3 Shadowing

The path loss model does not capture the varying environmental clutter at different

locations. However, measurements have shown that at any value d, the path loss

PL(d) at a particular location is random and distributed log-normally (normal in

dB) about the mean distance dependent values. Since the surrounding environmental

clutter may be different at different locations the path loss will be different than

the average value predicted by (2.4). This variation is mainly due to refraction anddiffraction off Interfering Objects (IO) in the path of the traveling signal, and is

an additive term to the path loss, with random values. This phenomenon is called

shadowing. It has a log-normal distribution about the mean path loss value [34].

Therefore the modified path loss expression is,

Pl(d) = PL(d) + X (2.5)

= PL(do) + (10np)Log10(d

do) + X (2.6)

Pr(d) = Pt(d) Pl(d); antenna gains included in Pl(d), (2.7)c Suvra Sekhar Das, 2007


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where X is zero mean Gaussian distributed random variable (in dB) with standard

deviation also in dB.

2.1.4 Small scale Fading

In small scale fading, the signal varies rapidly over a short distance. The variation

is caused by the multipath propagation of the received signal and the Doppler fre-

quency shift. The channel impulse response h(t, ) is a function of two variables, time

t and delay [35]. Due to some reflecting objects such as buildings, hills, trees, etc

some delayed versions of the transmitted signal, each with different amplitudes (Anp),

phases (n) arrive at the receiver at different delays (n). The parameters (amplitude,

phase, and delay) are random variables, and can be characterized by a channel im-pulse response. If unit impulse is transmitted and there are NSE scattering elements,

then the receiver would receive NSE different signals. Therefore, the channel impulse

response would be the sum of these NSE scattered signals as given below [36].

h(t, ) =

NSEm=1

Anp,m(t m)exp(jm). (2.8)

The channel impulse response is a function of time frequency and space [37]. A typical

channel impulse amplitude response over a region is shown in Fig. 2.3.

Amplitude

Delay

Spac

e/Time

RmsDelaySpread

MeanExcessDelay

MinimumSensitivityLevel

MaximumExcessDelay

Figure 2.3: Amplitude response

The relationship between the impulse response and the transfer function of the



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S(f, )d t

h(t, )t

T(t,f)

H(f,f)d

F

F-1

F-1

F-1

F-1

F F

F

Figure 2.4: channel impulse response and transfer function relationship

channel is shown in Fig. 2.4, where fd is Doppler frequency, is delay, t is time and

f is the Fourier domain representation of the delay.

2.1.4.1 Multipath Fading

Multipath propagation as shown in Fig. 2.5 gives rise to small scale fading in time

and frequency domain. The multipath properties of a given environment are usually

characterized by the power delay profile. Power delay profile denotes the average

power of each multipath. Figure 2.6 shows a typical power delay profile. When the

first multipath component has the highest power then it is a Ricean channel. Where as

when, the first path does not have the highest power which usually happens in non line

of sight scenario, then it is usually a Rayleigh channel and this is used in this thesis.

The power delay profile of a typical Rayleigh multipath propagation is shown to have

a exponential decay profile which is a commonly used model. [31]. There are several

other models which consider the cluster effect. i.e. there is a double exponential

decay, where each multipath is followed by a sequence of multipath during a very

short interval with a steeper decay constant. Another model for the delay profile has

the first few taps with same average power followed by exponential decay [38].

The channel impulse response is an instantaneous realization of the power delay

profile. A typical method of implementing it is via the Clarks methods as in [34].

Another method is the Rice method [39]. There exists other methods such as rayc Suvra Sekhar Das, 2007


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Figure 2.5: Multipath propagation

tracing models. The Clarks method has been mostly followed in this work. In some

cases the rayleighchan function of Matlab has been used. In these models, each

multipath component is generated so that they follow a desired Doppler spectrum.

The Doppler spectrum can be easily integrated into the system. Though for indoor

channels most of the taps in the models are supposed to have Jakes Doppler spectrum,

the outdoor multipath channel model taps can have mixed Doppler spectrum, i.e.

while some of the taps are advocated to use Jakess spectrum the one which are

towards the tail of the power delay profile may have Gauss spectrum, details which

can be found in [39]. Due to the multipath reflections a transmitted impulse gets time

dispersed, i.e. spread in time domain. A measure of this time spread phenomenon is

the mean excess delay, which is defined as [34]

m =

max0

E|h()|2 dmax0

E|h()|2 d(2.9)

where E denotes expectation, max is the maximum delay of the arriving multi paths,

h() is the component of the arriving multi path at a delay of. The rms delay spread



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Power

Delay

MaximmDelay

Firstmultipath

RmsDelaySpread

MeanExcessDelay

SensitivityLevel

Exponentiallydecayingenvelope

Figure 2.6: A typical power delay profile

of the channel is defined as

rms = 2 m2 (2.10)where

2 =

max0

2E|h()|2 dmax0

E|h()|2 d. (2.11)

The exponential power delay profile defines,

E|h()|2 =e0

max

0e0

, for 0 < < max

= 0, elsewhere. (2.12)

In the above 0 is the characteristic of the power delay. The rms delay spread is

the average information of a certain environment, but it is expected to have a local

variation over a few hundred nano seconds [31, 40].

The small scale channel model does not generate or absorb any power. i.e.max0

|h()|2 d = 1. (2.13)

This ensures that we are concerned only with short term multi path fading scenario.c Suvra Sekhar Das, 2007


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The Fourier transform of the channel impulse response is the channel transfer

function, as shown in Fig. 2.7. The signal experiences different levels of fading for dif-

30

25

20

15

10

5

0

5

Frequency

GainindB

Figure 2.7: Frequency domain channel response

ferent frequencies in the fading channel. With such characteristics, a fading channel

could be either frequency or non-frequency selective. This depends on the bandwidth

of the system compared with the channel coherence bandwidth, Bc. The coherence

bandwidth is defined as the frequency separation f such that the correlation coeffi-

cient falls below a defined real value between 0 and 1. B c is inversely proportional to

the rms delay spread RMS [41, 37].

Bc 1RMS

(2.14)

If the system bandwidth is much smaller compared to the coherence bandwidth,

then the channel is said to be frequency non selective. In this case, the correlation

coefficient2 of the sub carrier channel transfer function is almost 1 for the frequencies

within the system bandwidth. Physically, the frequency response within the system

bandwidth is almost flat, so it is also called flat fading. On the other hand, if the

2EH(f),H(f+f), where H is the channel frequency response at frequency f.



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300 200 100 0 100 2000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Figure 2.8: Power Spectral Density vs fre-quency of Jakes spectrum

300 200 100 0 100 200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Figure 2.9: Power Spectral Density vs fre-quency of typical Gauss spectrum

-7-5-3-11357

-7

-5

-3

-1

1

3

5

7

I

Q

Figure 2.10: Signal space diagram for rectangular 64-QAM

On the other hand, the term Phase Shift Keying (PSK) implies the phase of the

modulated waveform has the source information. QAM uses the combination of both

these techniques and carries information in the phase as well as the amplitude of the

modulated waveform. This can also be seen as embedding two simultaneous sequences

of k bits information signal on two quadrature carriers cos2fct and sin2fct. The

corresponding modulated waveform can be written as [35]:

sm(t) = (Amc + jAms)g(t)ej2fct m = 1, 2,...M (2.17)

where Amc and Ams are the information-bearing signal amplitudes of the quadra-

ture carrier and g(t) is the signal pulse.



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2.2 Quadrature Amplitude Modulation 25

The signal space diagram of rectangular QAM for different values of M is

shown in Figure 2.10 [35], where M = 2k and k is the number of information bits per

modulated symbol. It is common practice to have rectangular QAM where M = 2

2j

,with each symbol representing 2j information bits, because it has the advantage of

being generated as superposition of two PAM signal on quadrature carriers.



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2.3 OFDM

2.3.1 OFDM FundamentalsOFDM is an advanced form of Frequency Division Multiplexing (FDM) where the

frequencies multiplexed are orthogonal to each other and their spectra overlap with

the neighboring carriers. In a standard FDM system the sub carriers do not overlap

as shown in Fig. 2.11 which represents the amplitude frequency response of such

systems. OFDM is built on the principle of overlapping orthogonal sub carriers. The

frequency domain view of the signal is shown in Fig. 2.12. The peak of one sub

carrier coincides with the nulls of the other sub carriers due to the orthogonality.

Thus there is no interference from other sub carriers at the peak of a desired subcarrier even though the sub carrier spectrums overlap. It can be understood that

Amplitude

Frequency

GuardBand

SubcarrierbandwidthSubcarrierbandwidth

Figure 2.11: Non orthogonal carriers

SubCarrierPeaks

SubcarrierNullsSubCarrierSpacing/

Bandwidth

Frequency

Amplitude

Figure 2.12: Orthogonal Sub carriers in Multi carrier systems (OFDM)

OFDM systems avoid the loss in bandwidth efficiency prevalent in system using non

orthogonal carrier set. This brings in huge benefit in spectral efficiency for OFDM

systems over earlier systems.



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